Abstract
Since early 2000, Microfinance Institutions (MFI) have been using credit scoring for their risk assessment. However, one of the main problems of credit scoring in microfinance is the lack of structured financial data. To address this problem, MFI have started using non-traditional data which can be extracted from the digital footprint of their users. The non-traditional data can be used to build algorithms that can identify good borrowers as in traditional banking. This paper proposes an assembled method to evaluate the predictive power of the non-traditional method. By using the Weight of Evidence (WoE), a transformation based on the distribution within the feature, as feature transformation method, and then applying extremely randomized trees for feature selection, we were able to improve the accuracy of the credit scoring model by 20.20% when compared to the credit scoring model built with the traditional implementation of WoE. This paper shows how the assembling of WoE with different feature selection criteria can result in more robust credit scoring models in microfinance.

Abstract
uebe.Q is a managing software for solid referential information systems, such as ISO 9000 (for quality) and ISO 1400 (for environment). This is a long-term developed software, encompassing extensive and solid business logic with a long and successful record of deployments. A recent business model change imposed that the evolution and configuration of the software, shifts from the company (and especially the development team) to consultants and other business partners, along with the fact that different systems and respective data/information need to be integrated with minimal intervention of the development team. The so far acceptable rigidity, fragility, immobility and opacity of the software became a problem. Especially, the system was prepared to deal with a specific database respecting a specific schema and code-defined semantics. This paper describes the approach taken to overcome the problems derived form the previous architecture, by adopting (i) ontologies for the specification of business concepts and (ii) an information-integration Decision Support System (DSS) for mapping the domain specific ontologies to the database schemas. © 2020, Springer Nature Switzerland AG.

Abstract
Worldwide, family businesses are one of the cornerstones of the entrepreneurial fabric, being as a consequence central to growth and development. In a globalized era, these institutions require the attention of businessmen, practitioners, and policymakers. The chapter seeks to examine if the internationalization performance does vary according to firm size, and its link to the innovative performance in multiple dimensions along with conventional characteristics such as age and turnover. Theoretical research evidences the interest in understanding the patterns and determinants of the internationalisation performance, given its importance in firm growth and survival; however, this strategical option brings advantages and problems. Empirical evidence demonstrates that the determinants do change according to firm dimension; estimations provide valuable insights about the connection between globalized operation and innovation, for the different organisations. © 2021 by IGI Global. All rights reserved.

Abstract
The Web Ontology Language (OWL) is a World Wide Web Consortium standard, based on the Resource Description Format standard. It is used to define ontologies. While large ontologies are useful for different applications, some tools require partial ontologies, based mostly on a hierarchical relationship of classes. In this article we present bOWL, a basic OWL browser, with the main goal of being pluggable into others, more significant, web applications. The tool was tested through its integration on LeXmart, a dictionary editing tool.

Abstract
This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.

Abstract
The wine industry has a high business volume and adds value to the economy. This chapter intends to predict the wine firm performance of three of the most relevant Portuguese regions, by resorting to data available on the Portuguese Farm Accountancy Data Network (PTFADN, Resultados médios por exploração. Available on http://www.gpp.pt/index.php/rica/rede-de-informacao-de-contabilidades-agricolas-rica. Accessed 13 Mar 2018, 2001–2015). The existing social, economic and environmental parameters allowed us to perform function fitting with MATLAB, in order to attain information about the variable’s behaviour. Through the Agent-Based Model (ABM) simulations, it is possible to realize that, in general, the Alentejo region is substantially well prepared to deal with negative scenarios when compared with North and Central regions. Alternative scenarios can be performed in order to develop overall governmental policy recommendations, so as to ensure the sustainability of the three regions. © 2020, The Author(s).